A Novel Framework for Gauging Information Extracted from  Smartphones Using Neutrosophic Logic

R. M. Abou alzahab; Amr Ismail; S. H. Abd Elkhalik; H. M. El-Bakry; A. A.  Salama

Authors

R. M. Abou alzahab Department of Mathematics and Computer Science, Faculty of Science, Port Said University, Egypt,
Amr Ismail Department of Mathematics and Computer Science, Faculty of Science, Port Said University, Egypt,
S. H. Abd Elkhalik Department. of Communication and Computer Engineering, Air Defense University., Egypt
H. M. El-Bakry Department of Information Systems., Faculty of Computer and Information Science, Mansoura University., Egypt
A. A. Salama Department of Mathematics and Computer Science, Faculty of Science, Port Said University, Egypt,

Keywords:

Smartphone, Forensics, Neutrosophic Logic, Fuzzy Logic, Uncertainty, Investigations.

Abstract

Smartphones contain a vast amount of information about their users, which can be used as
evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic
investigators to identify and use the most relevant information. Neutrosophic logic is a
generalization of fuzzy logic that allows for uncertainty and vagueness, making it a more potent tool
for dealing with the ambiguity of real-world data. The proposed framework aims to identify and
utilize the most relevant information for forensic investigators, making it easier to solve criminal
cases using Neutrosophic logic. This novel approach provides a more powerful tool for dealing with
the ambiguity of smartphone data, ultimately improving the accuracy and effectiveness of forensic
investigations. Our research has utilized Neutrosophic logic to evaluate the degree of truth, falsity,
or Indeterminacy of this information. Additionally, this study analyzes conversations between
individuals using Excel’s fuzzy lookup add-in to determine the percentage of truth and false in each
conversation. The results were compiled into a dataset and utilized a Neutrosophic Python code to
evaluate the information. The results indicate the percentage of truth, false, and Indeterminacy in
each conversation and which can be used to determine its admissibility as evidence and which not.

DOI: 10.5281/zenodo.13988374